OpenRaiser/Intern-Atlas huggingface dataset:
Constructing Variables Using Classifiers as an Aid to Regression: An Empirical Assessment
Venue: arXiv cs.LG 2024
Year: 2024

This paper proposes a method for the automatic creation of variables (in the case of regression) that complement the information contained in the initial input vector. The method works as a pre-processing step in which the continuous values of the variable to be regressed are discretized into a set of intervals which are then used to define value thresholds. Then classifiers are trained to predict whether the value to be regressed is less than or equal to each of these thresholds. The different outputs of the classifiers are then concatenated in the form of an additional vector of variables that enriches the initial vector of the regression problem. The implemented system can thus be considered as a generic pre-processing tool. We tested the proposed enrichment method with 5 types of regressors and evaluated it in 33 regression datasets. Our experimental results confirm the interest of the approach.
DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation
Venue: arXiv cs.CV 2024
Year: 2024

World models have demonstrated superiority in autonomous driving, particularly in the generation of multi-view driving videos. However, significant challenges still exist in generating customized driving videos. In this paper, we propose DriveDreamer-2, which builds upon the framework of DriveDreamer and incorporates a Large Language Model (LLM) to generate user-defined driving videos. Specifically, an LLM interface is initially incorporated to convert a user's query into agent trajectories. Subsequently, a HDMap, adhering to traffic regulations, is generated based on the trajectories. Ultimately, we propose the Unified Multi-View Model to enhance temporal and spatial coherence in the generated driving videos. DriveDreamer-2 is the first world model to generate customized driving videos, it can generate uncommon driving videos (e.g., vehicles abruptly cut in) in a user-friendly manner. Besides, experimental results demonstrate that the generated videos enhance the training of driving perception methods (e.g., 3D detection and tracking). Furthermore, video generation quality of DriveDreamer-2 surpasses other state-of-the-art methods, showcasing FID and FVD scores of 11.2 and 55.7, representing relative improvements of 30% and 50%.
Surface-aware Mesh Texture Synthesis with Pre-trained 2D CNNs
Venue: arXiv cs.GR 2024
Year: 2024

Mesh texture synthesis is a key component in the automatic generation of 3D content. Existing learning-based methods have drawbacks -- either by disregarding the shape manifold during texture generation or by requiring a large number of different views to mitigate occlusion-related inconsistencies. In this paper, we present a novel surface-aware approach for mesh texture synthesis that overcomes these drawbacks by leveraging the pre-trained weights of 2D Convolutional Neural Networks (CNNs) with the same architecture, but with convolutions designed for 3D meshes. Our proposed network keeps track of the oriented patches surrounding each texel, enabling seamless texture synthesis and retaining local similarity to classical 2D convolutions with square kernels. Our approach allows us to synthesize textures that account for the geometric content of mesh surfaces, eliminating discontinuities and achieving comparable quality to 2D image synthesis algorithms. We compare our approach with state-of-the-art methods where, through qualitative and quantitative evaluations, we demonstrate that our approach is more effective for a variety of meshes and styles, while also producing visually appealing and consistent textures on meshes.
Development of a Reliable and Accessible Caregiving Language Model (CaLM)
Venue: arXiv cs.CL 2024
Year: 2024

Unlike professional caregivers, family caregivers often assume this role without formal preparation or training. Because of this, there is an urgent need to enhance the capacity of family caregivers to provide quality care. Large language models can potentially be used as a foundation technology for supporting caregivers as educational tools or as adjunct to care. This study aimed to develop a reliable Caregiving Language Model (CaLM) by using FMs and a caregiving knowledge base, develop an accessible CaLM using a small FM that requires fewer computing resources, and evaluate the performance of the model compared to a large FM. We developed CaLM using the Retrieval Augmented Generation (RAG) framework combined with FM fine-tuning for improving the quality of FM answers by grounding the model on a caregiving knowledge base. We used two small FMs as candidates for the FM of CaLM (LLaMA-2 and Falcon with 7B parameters) and larger FM GPT-3.5 as a benchmark. We developed the caregiving knowledge base by gathering various types of documents from the Internet. In this study, we focused on caregivers of individuals with Alzheimer's Disease Related Dementias. We evaluated the models' performance using the benchmark metrics commonly used in evaluating language models and their reliability to provide accurate references with the answers. The RAG framework improved the performance of all FMs used in this study across all measures. As expected, the large FM performed better than small FMs across all metrics. The most interesting result is that small fine-tuned FMs with RAG performed significantly better than GPT 3.5 across all metrics. The fine-tuned LLaMA-2 small FM performed better than GPT 3.5 (even with RAG) in returning references with the answers. The study shows that reliable and accessible CaLM can be developed by using small FMs with a knowledge base specific to the caregiving domain.
QUASAR: QUality and Aesthetics Scoring with Advanced Representations
Venue: arXiv cs.CV 2024
Year: 2024

This paper introduces a new data-driven, non-parametric method for image quality and aesthetics assessment, surpassing existing approaches and requiring no prompt engineering or fine-tuning. We eliminate the need for expressive textual embeddings by proposing efficient image anchors in the data. Through extensive evaluations of 7 state-of-the-art self-supervised models, our method demonstrates superior performance and robustness across various datasets and benchmarks. Notably, it achieves high agreement with human assessments even with limited data and shows high robustness to the nature of data and their pre-processing pipeline. Our contributions offer a streamlined solution for assessment of images while providing insights into the perception of visual information.
Monitoring the Venice Lagoon: an IoT Cloud-Based Sensor Nerwork Approach
Venue: arXiv cs.NI 2024
Year: 2024

Monitoring the coastal area of the Venice Lagoon is of significant importance. While the impact of global warming is felt worldwide, coastal and littoral regions bear the brunt more prominently. These areas not only face the threat of rising sea levels but also contend with the escalating occurrence of seaquakes and floods. Additionally, the intricate ecosystems of rivers, seas, and lakes undergo profound transformations due to climate change and pollutants. Employing devices like the SENSWICH floating wireless sensor presented in this article and similar measurement instruments proves invaluable to automate environmental monitoring, hence eliminating the need for manual sampling campaigns. The utilization of wireless measurement devices offers cost-effectiveness, real-time analysis, and a reduction in human resource requirements. Storing data in cloud services further enhances the ability to monitor parameter changes over extended time intervals. In this article, we present an enhanced sensing device aimed at automating water quality assessment, while considering power consumption and reducing circuit complexity. Specifically, we will introduce the new schematic and circuit of SENSWICH which had changes in circuit and electronic aspects. Furthermore, we outline the methodology for aggregating data in a cloud service environment, such as Amazon Web Service (AWS), and using Grafana for visualization.
TCAM-SSD: A Framework for Search-Based Computing in Solid-State Drives
Venue: arXiv cs.AR 2024
Year: 2024

As the amount of data produced in society continues to grow at an exponential rate, modern applications are incurring significant performance and energy penalties due to high data movement between the CPU and memory/storage. While processing in main memory can alleviate these penalties, it is becoming increasingly difficult to keep large datasets entirely in main memory. This has led to a recent push for in-storage computation, where processing is performed inside the storage device. We propose TCAM-SSD, a new framework for search-based computation inside the NAND flash memory arrays of a conventional solid-state drive (SSD), which requires lightweight modifications to only the array periphery and firmware. TCAM-SSD introduces a search manager and link table, which can logically partition the NAND flash memory's contents into search-enabled regions and standard storage regions. Together, these light firmware changes enable TCAM-SSD to seamlessly handle block I/O operations, in addition to new search operations, thereby reducing end-to-end execution time and total data movement. We provide an NVMe-compatible interface that provides programmers with the ability to dynamically allocate data on and make use of TCAM-SSD, allowing the system to be leveraged by a wide variety of applications. We evaluate three example use cases of TCAM-SSD to demonstrate its benefits. For transactional databases, TCAM-SSD can mitigate the performance penalties for applications with large datasets, achieving a 60.9% speedup over a conventional system that retrieves data from the SSD and computes using the CPU. For database analytics, TCAM-SSD provides an average speedup of 17.7x over a conventional system for a collection of analytical queries. For graph analytics, we combine TCAM-SSD's associative search with a sparse data structure, speeding up graph computing for larger-than-memory datasets by 14.5%.
MRL Parsing Without Tears: The Case of Hebrew
Venue: arXiv cs.CL 2024
Year: 2024

Syntactic parsing remains a critical tool for relation extraction and information extraction, especially in resource-scarce languages where LLMs are lacking. Yet in morphologically rich languages (MRLs), where parsers need to identify multiple lexical units in each token, existing systems suffer in latency and setup complexity. Some use a pipeline to peel away the layers: first segmentation, then morphology tagging, and then syntax parsing; however, errors in earlier layers are then propagated forward. Others use a joint architecture to evaluate all permutations at once; while this improves accuracy, it is notoriously slow. In contrast, and taking Hebrew as a test case, we present a new "flipped pipeline": decisions are made directly on the whole-token units by expert classifiers, each one dedicated to one specific task. The classifiers are independent of one another, and only at the end do we synthesize their predictions. This blazingly fast approach sets a new SOTA in Hebrew POS tagging and dependency parsing, while also reaching near-SOTA performance on other Hebrew NLP tasks. Because our architecture does not rely on any language-specific resources, it can serve as a model to develop similar parsers for other MRLs.
Exact algorithms and heuristics for capacitated covering salesman problems
Venue: arXiv cs.AI 2024
Year: 2024

This paper introduces the Capacitated Covering Salesman Problem (CCSP), approaching the notion of service by coverage in capacitated vehicle routing problems. In CCSP, locations where vehicles can transit are provided, some of which have customers with demands. The objective is to service customers through a fleet of vehicles based in a depot, minimizing the total distance traversed by the vehicles. CCSP is unique in the sense that customers, to be serviced, do not need to be visited by a vehicle. Instead, they can be serviced if they are within a coverage area of the vehicle. This assumption is motivated by applications in which some customers are unreachable (e.g., forbidden access to vehicles) or visiting every customer is impractical. In this work, optimization methodologies are proposed for the CCSP based on ILP (Integer Linear Programming) and BRKGA (Biased Random-Key Genetic Algorithm) metaheuristic. Computational experiments conducted on a benchmark of instances for the CCSP evaluate the performance of the methodologies with respect to primal bounds. Furthermore, our ILP formulation is extended in order to create a novel MILP (Mixed Integer Linear Programming) for the Multi-Depot Covering Tour Vehicle Routing Problem (MDCTVRP). Computational experiments show that the extended MILP formulation outperformed the previous state-of-the-art exact approach with respect to optimality gaps. In particular, optimal solutions were obtained for several previously unsolved instances.
On the stochastics of human and artificial creativity
Venue: arXiv cs.AI 2024
Year: 2024

What constitutes human creativity, and is it possible for computers to exhibit genuine creativity? We argue that achieving human-level intelligence in computers, or so-called Artificial General Intelligence, necessitates attaining also human-level creativity. We contribute to this discussion by developing a statistical representation of human creativity, incorporating prior insights from stochastic theory, psychology, philosophy, neuroscience, and chaos theory. This highlights the stochastic nature of the human creative process, which includes both a bias guided, random proposal step, and an evaluation step depending on a flexible or transformable bias structure. The acquired representation of human creativity is subsequently used to assess the creativity levels of various contemporary AI systems. Our analysis includes modern AI algorithms such as reinforcement learning, diffusion models, and large language models, addressing to what extent they measure up to human level creativity. We conclude that these technologies currently lack the capability for autonomous creative action at a human level.
Designing Wearable Augmented Reality Concepts to Support Scalability in Autonomous Vehicle-Pedestrian Interaction
Venue: arXiv cs.HC 2024
Year: 2024

Wearable augmented reality (AR) offers new ways for supporting the interaction between autonomous vehicles (AVs) and pedestrians due to its ability to integrate timely and contextually relevant data into the user's field of view. This article presents novel wearable AR concepts that assist crossing pedestrians in multi-vehicle scenarios where several AVs frequent the road from both directions. Three concepts with different communication approaches for signaling responses from multiple AVs to a crossing request, as well as a conventional pedestrian push button, were simulated and tested within a virtual reality environment. The results showed that wearable AR is a promising way to reduce crossing pedestrians' cognitive load when the design offers both individual AV responses and a clear signal to cross. The willingness of pedestrians to adopt a wearable AR solution, however, is subject to different factors, including costs, data privacy, technical defects, liability risks, maintenance duties, and form factors. We further found that all participants favored sending a crossing request to AVs rather than waiting for the vehicles to detect their intentions-pointing to an important gap and opportunity in the current AV-pedestrian interaction literature.
Adaptive Hyperparameter Optimization for Continual Learning Scenarios
Venue: arXiv cs.LG 2024
Year: 2024

Hyperparameter selection in continual learning scenarios is a challenging and underexplored aspect, especially in practical non-stationary environments. Traditional approaches, such as grid searches with held-out validation data from all tasks, are unrealistic for building accurate lifelong learning systems. This paper aims to explore the role of hyperparameter selection in continual learning and the necessity of continually and automatically tuning them according to the complexity of the task at hand. Hence, we propose leveraging the nature of sequence task learning to improve Hyperparameter Optimization efficiency. By using the functional analysis of variance-based techniques, we identify the most crucial hyperparameters that have an impact on performance. We demonstrate empirically that this approach, agnostic to continual scenarios and strategies, allows us to speed up hyperparameters optimization continually across tasks and exhibit robustness even in the face of varying sequential task orders. We believe that our findings can contribute to the advancement of continual learning methodologies towards more efficient, robust and adaptable models for real-world applications.
Contemplating Secure and Optimal Design Practices for Information Infrastructure From a Human Factors Perspective
Venue: arXiv cs.CR 2024
Year: 2024

Designing secure information infrastructure is a function of design and usability. However, security is seldom given priority when systems are being developed. Secure design practices should balance between functionality (i.e., proper design) to meet minimum requirements and user-friendliness. Design recommendations such as those with a user-centric approach (i.e., inclusive of only relevant information, user liberty) and presenting information within its proper context in a clear and engaging manner has been scientifically shown to improve user response and experience.
FWin transformer for dengue prediction under climate and ocean influence
Venue: arXiv cs.LG 2024
Year: 2024

Dengue fever is one of the most deadly mosquito-born tropical infectious diseases. Detailed long range forecast model is vital in controlling the spread of disease and making mitigation efforts. In this study, we examine methods used to forecast dengue cases for long range predictions. The dataset consists of local climate/weather in addition to global climate indicators of Singapore from 2000 to 2019. We utilize newly developed deep neural networks to learn the intricate relationship between the features. The baseline models in this study are in the class of recent transformers for long sequence forecasting tasks. We found that a Fourier mixed window attention (FWin) based transformer performed the best in terms of both the mean square error and the maximum absolute error on the long range dengue forecast up to 60 weeks.
A Model for Assessing Network Asset Vulnerability Using QPSO-LightGBM
Venue: arXiv cs.CR 2024
Year: 2024

With the continuous development of computer technology and network technology, the scale of the network continues to expand, the network space tends to be complex, and the application of computers and networks has been deeply into politics, the military, finance, electricity, and other important fields. When security events do not occur, the vulnerability assessment of these high-risk network assets can be actively carried out to prepare for rainy days, to effectively reduce the loss caused by security events. Therefore, this paper proposes a multi-classification prediction model of network asset vulnerability based on quantum particle swarm algorithm-Lightweight Gradient Elevator (QPSO-LightGBM). In this model, based on using the Synthetic minority oversampling technique (SMOTE) to balance the data, quantum particle swarm optimization (QPSO) was used for automatic parameter optimization, and LightGBM was used for modeling. Realize multi-classification prediction of network asset vulnerability. To verify the rationality of the model, the proposed model is compared with the model constructed by other algorithms. The results show that the proposed model is better in various predictive performance indexes.
Multiple Population Alternate Evolution Neural Architecture Search
Venue: arXiv cs.NE 2024
Year: 2024

The effectiveness of Evolutionary Neural Architecture Search (ENAS) is influenced by the design of the search space. Nevertheless, common methods including the global search space, scalable search space and hierarchical search space have certain limitations. Specifically, the global search space requires a significant amount of computational resources and time, the scalable search space sacrifices the diversity of network structures and the hierarchical search space increases the search cost in exchange for network diversity. To address above limitation, we propose a novel paradigm of searching neural network architectures and design the Multiple Population Alternate Evolution Neural Architecture Search (MPAE), which can achieve module diversity with a smaller search cost. MPAE converts the search space into L interconnected units and sequentially searches the units, then the above search of the entire network be cycled several times to reduce the impact of previous units on subsequent units. To accelerate the population evolution process, we also propose the the population migration mechanism establishes an excellent migration archive and transfers the excellent knowledge and experience in the migration archive to new populations. The proposed method requires only 0.3 GPU days to search a neural network on the CIFAR dataset and achieves the state-of-the-art results.
DrJAX: Scalable and Differentiable MapReduce Primitives in JAX
Venue: arXiv cs.DC 2024
Year: 2024

We present DrJAX, a JAX-based library designed to support large-scale distributed and parallel machine learning algorithms that use MapReduce-style operations. DrJAX leverages JAX's sharding mechanisms to enable native targeting of TPUs and state-of-the-art JAX runtimes, including Pathways. DrJAX embeds building blocks for MapReduce computations as primitives in JAX. This enables three key benefits. First, DrJAX computations can be translated directly to XLA HLO, enabling flexible integration with a wide array of ML training platforms. Second, DrJAX computations are fully differentiable. Last, DrJAX computations can be interpreted out to existing batch-processing compute systems, including traditional MapReduce systems like Apache Beam and cross-device compute systems like those powering federated learning applications. We show that DrJAX provides an easily programmable, performant, and scalable framework for parallelized algorithm development. DrJAX is available at \url{https://github.com/google-research/google-research/tree/master/drjax}.
On the Limited Representational Power of Value Functions and its Links to Statistical (In)Efficiency
Venue: arXiv cs.LG 2024
Year: 2024

Identifying the trade-offs between model-based and model-free methods is a central question in reinforcement learning. Value-based methods offer substantial computational advantages and are sometimes just as statistically efficient as model-based methods. However, focusing on the core problem of policy evaluation, we show information about the transition dynamics may be impossible to represent in the space of value functions. We explore this through a series of case studies focused on structures that arises in many important problems. In several, there is no information loss and value-based methods are as statistically efficient as model based ones. In other closely-related examples, information loss is severe and value-based methods are severely outperformed. A deeper investigation points to the limitations of the representational power as the driver of the inefficiency, as opposed to failure in algorithm design.